[Vp-integration-subgroup] Another case study of model integration

LUCAS BOETTCHER lucasb at g.ucla.edu
Thu Apr 29 01:10:44 PDT 2021 

Hi Robin

Interesting paper! Thanks for sharing.

Another related study just appeared in EJE a few days ago:
https://link.springer.com/article/10.1007/s10654-021-00746-4

Best

Lucas


On Thu, Apr 29, 2021 at 9:59 AM Robin Thompson <robin.thompson1988 at gmail.com>
wrote:

> Hi Jacob,
>
> Thanks for this. All the generation time distributions (i.e. expected
> infectiousness as a function of the time since infection) that we have been
> considering are normalised so that they integrate to 1.  In fact, our
> manuscript in which we estimate the SARS-CoV-2 generation time distribution
> using a few different models was accepted by eLife earlier this week. The
> accepted manuscript (pre journal formatting) is available at:
>
> https://elifesciences.org/articles/65534
>
> Figure 2A (at the end of the manuscript) shows the inferred generation
> time distribution for four different models. The purple dotted one involves
> the standard assumption that the generation time and incubation period are
> independent.
>
> If anyone in the WG has any comments on the manuscript, then please do get
> in touch :-)
>
> Thanks, and best wishes,
> Robin
> ----------------------------------------------------------------------
> Dr Robin Thompson
> Assistant Professor of Mathematical Epidemiology
> Mathematics Institute
> University of Warwick, UK
> www.robin-thompson.co.uk
> ----------------------------------------------------------------------
>
> On 29 Apr 2021, at 07:48, Jacob Barhak <jacob.barhak at gmail.com> wrote:
>
> Yes Lucas,
>
> You are right.
>
> All the infectiousness models are presented here as portions from maximal
> infectiousness not as density functions.
>
> Each of the models had a different definition and to compare them,  a
> common definition is needed.
>
> The definition I asked also matches the definition used in my ensemble. I
> still have not integrated it in the ensemble,  yet I am about to do it
> today,  yet simulations will take more time. And thank you for allowing to
> release this under CC0.
>
> Hopefully this documentation is clear enough and the comparison is useful.
> The fact that your model resembles another and I assume you started from
> different assumptions,  may have importance. I highly recommend you connect
> with the other modelers that modeled infectiousness used here and discuss
> the differences. it may lead to better understanding.
>
> I know Robin is on this mailing list. Hopefully he will choose to comment.
>
>
>         Jacob
>
>
>
>
>
> On Thu, Apr 29, 2021, 01:32 LUCAS BOETTCHER <lucasb at g.ucla.edu> wrote:
>
>> Hi Jacob
>>
>> Thanks for integrating our infectiousness model into your modeling
>> framework!
>>
>> One point I am not so sure about is the normalization of these different
>> distributions.
>>
>> If t is the time since infection, the distribution in our paper is
>> normalized such that the integration over t from 0 to infinity yields 1. I
>> think that other distributions are normalized in a different way, or not
>> normalized at all? For example, figure 3G (Ke et al) does not seem to be
>> normalized in the same way as our PDF?
>>
>> Best
>>
>> Lucas
>>
>>
>>
>>
>>
>>
>>
>>
>> On Wed, Apr 28, 2021 at 1:33 PM Jacob Barhak <jacob.barhak at gmail.com>
>> wrote:
>>
>>> Hi Lucas,
>>>
>>> You may wish to compare your infectiousness model to the one generated
>>> by Will Hart and Robin Thompson. The are the closest ones from the ones I
>>> implemented and made available here:
>>>
>>> https://github.com/Jacob-Barhak/COVID19Models/tree/main/COVID19_Infectiousness_Multi
>>>
>>> If you download the html file alone to your machine, you should be able
>>> to view all models.
>>>
>>> Please note that I had issues with the umlaut 'o' character in your name
>>> since I wanted to avoid Unicode issues, so I spelled your last name as
>>> Bottcher - please let me know if you want it changed - I am sure you see
>>> this problem a lot and may have a preference.
>>>
>>> Hopefully you like the comparison to other potential models.
>>>
>>> I will proceed with integrating this model into my ensemble.
>>>
>>>            Jacob
>>>
>>>
>>>
>>> On Tue, Mar 30, 2021 at 1:23 PM Jacob Barhak <jacob.barhak at gmail.com>
>>> wrote:
>>>
>>>> Greetings Integration sub-group,
>>>>
>>>> Below you will find another attempt to integrate a few models created
>>>> by Lucas Boettcher into a COVID-19 model.
>>>>
>>>> Those interested in following the details you will find our
>>>> correspondence in that thread to show difficulties in integrating models.
>>>>
>>>> I will attempt to summarize for those with little time to follow back
>>>> details.
>>>>
>>>> Lucas had several models that we attempted to reuse:
>>>> - Recovery model and incubation model based on Singapore data
>>>> - Several mortality models - one based on CDC data
>>>> - An infectiousess model based on a previous version of
>>>> https://doi.org/10.1038/s41591-020-0869-5
>>>>
>>>> So far, after roughly 2 weeks of correspondence we were able to:
>>>> 1. transmit the infectiousness profile and make sure I can implement it
>>>> properly - trace it back to data to make sure it is reusable. Note that in
>>>> this case we were using the same language - python and still transmission
>>>> of the formula was not straightforward since there was ambiguity in forms
>>>> of the function that can be defined in different ways.
>>>>
>>>> 2. Determine that Recovery / incubation models cannot be reused
>>>> currently since the data source that made the data available is not
>>>> responding and did not specify usage terms. I asked assistance from this
>>>> mailing list to contact the entity responsible for the data in this
>>>> message:
>>>> https://lists.simtk.org/pipermail/vp-integration-subgroup/2021-March/000043.html
>>>>
>>>> If you can help, please respond.
>>>>
>>>> 3. The Mortality model was not fully defined and I will wait for
>>>> publication of the preprint - hopefully Lucase will transmit it to this
>>>> mailing list. However, I highly suggest people look at his paper that
>>>> discusses mortality - it shows some important aspects of counting numbers
>>>> and how confusing something as reported  mortality numbers can be.  You can
>>>> find the paper here:
>>>> https://doi.org/10.1088/1478-3975/ab9e59
>>>>
>>>> For those interested in the fine details - please keep on reading the
>>>> correspondence below in reverse chronological order.
>>>>
>>>> Feedback from subgroup members will be appreciated.
>>>>
>>>>             Jacob
>>>>
>>>>
>>>>
>>>>
>>>> On Tue, Mar 30, 2021 at 8:21 AM LUCAS BOETTCHER <lucasb at g.ucla.edu>
>>>> wrote:
>>>>
>>>>> Hi Jacob
>>>>>
>>>>> Yes, please feel free to add our discussion to the mailing list.
>>>>>
>>>>> Best
>>>>>
>>>>> Lucas
>>>>>
>>>>> On Tue, Mar 30, 2021 at 10:37 AM Jacob Barhak <jacob.barhak at gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Thanks Lucas,
>>>>>>
>>>>>> These are all good news. Since the recovery function is associated
>>>>>> with the Singapore data, then we can hold with it until we authenticate the
>>>>>> data.
>>>>>>
>>>>>> The infectiousness curve you mentioned is based on an article stating
>>>>>> that there is no restriction on data access in the data availability
>>>>>> section.- yet it would be nice to write a note to the authors about using
>>>>>> their data - it is good scholarship - and I noticed that those authors
>>>>>> actually correspond - look at the correction to their paper. So it would be
>>>>>> nice to write them an email indicating their data was useful. I think their
>>>>>> correction does not involve data change, so if you used their data, you
>>>>>> should be fine - yet it is worth another check
>>>>>>
>>>>>> I will wait for your mortality presprint when it is available.
>>>>>>
>>>>>> I think the discussion in this thread is good enough to go public in
>>>>>> the maling list as it seems to me now - so if you approve, I will add the
>>>>>> integration mailing list to the recipient list and summarize the
>>>>>> difficulties in integration we encountered. It is important people can see
>>>>>> with their own eyes the difficulties as they appear in practice. Hopefully
>>>>>> those cases will help support methods that will improve things in the long
>>>>>> run.
>>>>>>
>>>>>> I hope you still approve of this going public.
>>>>>>
>>>>>>           Jacob
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Tue, Mar 30, 2021 at 2:16 AM LUCAS BOETTCHER <lucasb at g.ucla.edu>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi Jacob
>>>>>>>
>>>>>>> Yes, I'll try to clarify some points below.
>>>>>>>
>>>>>>> On Mon, Mar 29, 2021 at 9:31 PM Jacob Barhak <jacob.barhak at gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Thanks Lucas,
>>>>>>>>
>>>>>>>> You will have to bear with me. The amount of information you
>>>>>>>> transmitted is actually non trivial and as much as you tried to
>>>>>>>> communicate it clearly it is just too much condensed in one email. I
>>>>>>>> already got confused as it seems.
>>>>>>>>
>>>>>>>> Allow me to clarify with a few questions:
>>>>>>>>
>>>>>>>> 1) the Singapore data and the python program you sent were for
>>>>>>>> recovery /  incubation and it is based on the singapore data - correct?
>>>>>>>>
>>>>>>> >> Yes, that's correct.
>>>>>>>
>>>>>>>
>>>>>>>> 2) The infectiousness curve we reconstructed is Eq (7) in your
>>>>>>>> mortality paper - What data did you fit it to? Is it also fittet on the
>>>>>>>> Singapore data?
>>>>>>>>
>>>>>>> >> We inferred this curve from the first (uncorrected) version of
>>>>>>> "He, X., Lau, E. H., Wu, P., Deng, X., Wang, J., Hao, X., ... & Leung, G.
>>>>>>> M. (2020). Temporal dynamics in viral shedding and transmissibility of
>>>>>>> COVID-19. *Nature medicine*, *26*(5), 672-675."
>>>>>>>
>>>>>>>>
>>>>>>>> 3) What is the equation for mortality I can use to plug in with
>>>>>>>> other mortality functions? I see Table 2 summarizing different formats to
>>>>>>>> calculate mortality, yet I need a more formal equation I can use that is a
>>>>>>>> function of parameters such as MortalityProbablityPDF(
>>>>>>>> TimeSinceInfectionInDays, AgeInYears).
>>>>>>>>
>>>>>>>> >> Our first mortality paper appeared when there was little
>>>>>>> knowledge about age and mortality characteristics. We proposed some
>>>>>>> functional forms, but I think that there are better estimates available
>>>>>>> now. We're about to finalize another manuscript with a more advanced
>>>>>>> temporal network model with age structure/age-dependent mortality and
>>>>>>> different communities. I will share the preprint with you as soon as
>>>>>>> possible.
>>>>>>>
>>>>>>>
>>>>>>>> If you used CDC data such as
>>>>>>>> https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm?s_cid=mm6912e2_w
>>>>>>>> then there are no restrictions on yuse sicne US governemtn data is
>>>>>>>> considered public domain in most cases - there are very rare case where
>>>>>>>> government provies a license since data was acquired from a 3rd party, yet
>>>>>>>> generally, in the US government publications have no copyright - in fact I
>>>>>>>> think it is similar in some other countries - yet I am not a lawyer - so it
>>>>>>>> is worth checking.
>>>>>>>>
>>>>>>> >> Ok, good to know.
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> I must admit that I already got confused from the amount of
>>>>>>>> information with the infectiousness data and in my mind associated it with
>>>>>>>> the Singapore data - hopefully it is not associated and can be reused.
>>>>>>>>
>>>>>>>>                Jacob
>>>>>>>>
>>>>>>>> On Mon, Mar 29, 2021 at 1:27 AM LUCAS BOETTCHER <lucasb at g.ucla.edu>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi Jacob
>>>>>>>>>
>>>>>>>>> Yes, let's proceed. The mortality datasets are taken from various
>>>>>>>>> statistical offices and the CDC.
>>>>>>>>>
>>>>>>>>> If you're mainly interested in US mortality statistics, we just
>>>>>>>>> have to contact the CDC and ask about these licencing issues.
>>>>>>>>>
>>>>>>>>> Best
>>>>>>>>>
>>>>>>>>> Lucas
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Sunday, March 28, 2021, Jacob Barhak <jacob.barhak at gmail.com>
>>>>>>>>> wrote:
>>>>>>>>> > Hi Lucas,
>>>>>>>>> > It seems that data from the Singapure web site cannot be
>>>>>>>>> verified - I sent an email to the mailing list in hope someone has a
>>>>>>>>> contact in Singapore that can help with verifying the data and its usage
>>>>>>>>> terms.
>>>>>>>>> > I suggest we wait a bit more and if we still cannot move forward
>>>>>>>>> with that data, we can focus on other elements I can reuse from your paper
>>>>>>>>> towards integration. I already have several infectiousness curves, so we
>>>>>>>>> can perhaps focus on mortality if this in not connected to the Singapore
>>>>>>>>> data.
>>>>>>>>> > I hope this makes sense to you and moves us forward.
>>>>>>>>> >              Jacob
>>>>>>>>> >
>>>>>>>>> >
>>>>>>>>> > On Wed, Mar 17, 2021 at 11:49 AM LUCAS BOETTCHER <
>>>>>>>>> lucasb at g.ucla.edu> wrote:
>>>>>>>>> >>
>>>>>>>>> >> Thanks for your comments! I checked everything; responses are
>>>>>>>>> below.
>>>>>>>>> >>
>>>>>>>>> >> On Wed, Mar 17, 2021 at 12:51 PM Jacob Barhak <
>>>>>>>>> jacob.barhak at gmail.com> wrote:
>>>>>>>>> >>>
>>>>>>>>> >>> Thanks Lucas,
>>>>>>>>> >>> This is a good discussion since it shows more aspects of
>>>>>>>>> integration difficulties.
>>>>>>>>> >>> First thanks for being specific about the use of the gamma
>>>>>>>>> function to calculate infectiousness. Yet even with your clarifications, it
>>>>>>>>> looks a bit confusing to me and I want to verify that I am not misusing it.
>>>>>>>>> Therefore let me confirm with you that reimplementation is correct by
>>>>>>>>> giving two values of x:
>>>>>>>>> >>> >>> import scipy
>>>>>>>>> >>> >>> from scipy.stats import gamma
>>>>>>>>> >>> >>> a=8
>>>>>>>>> >>> >>> b=1.25
>>>>>>>>> >>> >>> x=3
>>>>>>>>> >>> >>> b*gamma.pdf(b*x, a)
>>>>>>>>> >>> 0.060826670304049466
>>>>>>>>> >>> >>> x=4
>>>>>>>>> >>> >>> b*gamma.pdf(b*x, a)
>>>>>>>>> >>> 0.13055607869631744
>>>>>>>>> >>>
>>>>>>>>> >>> And please confirm that x in that example is time in days from
>>>>>>>>> infection.
>>>>>>>>> >>
>>>>>>>>> >>
>>>>>>>>> >> >>>>>> Yes, I can confirm both. The numbers are correct and x
>>>>>>>>> is the time [days] from infection.
>>>>>>>>> >>
>>>>>>>>> >>>
>>>>>>>>> >>> If this is correct, then for my own purposes, I will need to
>>>>>>>>> get the probability of infection for each day from 0 to 18 . so this
>>>>>>>>> should generate the following results:
>>>>>>>>> >>> >>> import numpy as np
>>>>>>>>> >>> >>> x= np.array(range(19))
>>>>>>>>> >>> >>> b*gamma.pdf(b*x, a)
>>>>>>>>> >>> array([0.00000000e+00, 3.38829695e-04, 1.24257706e-02,
>>>>>>>>> 6.08266703e-02,
>>>>>>>>> >>>        1.30556079e-01, 1.78360666e-01, 1.83104790e-01,
>>>>>>>>> 1.54333118e-01,
>>>>>>>>> >>>        1.12599032e-01, 7.35756677e-02, 4.40725870e-02,
>>>>>>>>> 2.46064656e-02,
>>>>>>>>> >>>        1.29628669e-02, 6.50380786e-03, 3.13034629e-03,
>>>>>>>>> 1.45367341e-03,
>>>>>>>>> >>>        6.54334483e-04, 2.86572345e-04, 1.22498638e-04])
>>>>>>>>> >>>
>>>>>>>>> >>> If this is a good enough approximation, then the question is
>>>>>>>>> what does the numbers I generate mean? I assume this is the infectiousness
>>>>>>>>> density that sums to 1 since:
>>>>>>>>> >>> >>> sum(b*gamma.pdf(b*x, a))
>>>>>>>>> >>> 0.9999137765146388
>>>>>>>>> >>>
>>>>>>>>> >>
>>>>>>>>> >> >>>>>> Right, this distribution is normalized to 1. If one
>>>>>>>>> wants to obtain an infection rate for a disease model one has to use the
>>>>>>>>> methods described in the mortality paper I forwarded you. Equation 17
>>>>>>>>> connects the infectiousness distribution with S0*R0, so one can fix the
>>>>>>>>> pre-factor in Eq. 16 using a given S0*R0 (which can be estimated) and Eq.
>>>>>>>>> 17.
>>>>>>>>> >>
>>>>>>>>> >> https://doi.org/10.1088/1478-3975/ab9e59
>>>>>>>>> >>
>>>>>>>>> >>> As for the data. This is a typical example of ambiguity with
>>>>>>>>> regards to reuse. The team that produced the data did not specify a license
>>>>>>>>> yet made the data available. Typically for academic purposes such data is
>>>>>>>>> considered fair use. However, since I am a sole proprietor - a for profit
>>>>>>>>> organization, then I have to be selective and inquire if I can reuse this
>>>>>>>>> data. Options are that:
>>>>>>>>> >>> 1. The authors wanted to make this data public domain and
>>>>>>>>> therefore there is no copyright statement on the web site
>>>>>>>>> >>> 2. The authors neglected to put a copyright / license since
>>>>>>>>> they are overworked and this was not the most important thing on their mind
>>>>>>>>> - they want the data to be useful, yet have not considered implications of
>>>>>>>>> reuse.
>>>>>>>>> >>> 3. The authors considered the issues and decided to release
>>>>>>>>> this like this - this situation is problematic since it makes reuse terms
>>>>>>>>> unclear
>>>>>>>>> >>> I suspect that the answer is one of the first two options, yet
>>>>>>>>> I think that this can be clarified by contacting the web site authors
>>>>>>>>> listed as UPCODE ACADEMY - their web site is:
>>>>>>>>> https://www.upcodeacademy.com/
>>>>>>>>> >>> I located their email to be:
>>>>>>>>> >>> hello at upcodeacademy.com
>>>>>>>>> >>>
>>>>>>>>> >>> I think we should ask them to be explicit about the data and
>>>>>>>>> ask to release it under CC0 to clear all doubts. Since you plan to upload
>>>>>>>>> the data to github, you rather know the license beforehand to make sure you
>>>>>>>>> properly define the license on Github.However, I will be happy to
>>>>>>>>> communicate with them for you.
>>>>>>>>> >>
>>>>>>>>> >> >> Ok, it would be great if you could clarify the Singapore
>>>>>>>>> data license. For my projects, I would just upload the data and specify the
>>>>>>>>> source. In your case it will be better to clarify the license type.
>>>>>>>>> >> I will send you a GitHub repo link later.
>>>>>>>>> >>
>>>>>>>>> >>>
>>>>>>>>> >>> Once you are ready with your github and remove the zip file,
>>>>>>>>> we can add the integration subgroup mailing list to the recipient list and
>>>>>>>>> make this conversation public. It shows again the difficulties with
>>>>>>>>> integration and how much effort and communication there should be. This is
>>>>>>>>> excellent for the subgroup.
>>>>>>>>> >>
>>>>>>>>> >> >> Ok, perfect. Thanks!
>>>>>>>>> >>
>>>>>>>>> >>>
>>>>>>>>> >>>                 Jacob
>>>>>>>>> >>>
>>>>>>>>> >>>
>>>>>>>>> >>>
>>>>>>>>> >>>
>>>>>>>>> >>>
>>>>>>>>> >>>
>>>>>>>>> >>>
>>>>>>>>> >>>
>>>>>>>>> >>> On Wed, Mar 17, 2021 at 3:21 AM LUCAS BOETTCHER <
>>>>>>>>> lucasb at g.ucla.edu> wrote:
>>>>>>>>> >>>>
>>>>>>>>> >>>> Hi Jacob
>>>>>>>>> >>>> Thanks for your comments!
>>>>>>>>> >>>>
>>>>>>>>> >>>> I directly respond to your comments below.
>>>>>>>>> >>>>
>>>>>>>>> >>>> On Tue, Mar 16, 2021 at 11:45 PM Jacob Barhak <
>>>>>>>>> jacob.barhak at gmail.com> wrote:
>>>>>>>>> >>>>>
>>>>>>>>> >>>>> Many thanks Lucas,
>>>>>>>>> >>>>> This makes much more sense now.
>>>>>>>>> >>>>> However, just to show the subgroup that integration and
>>>>>>>>> reproducibility is still difficult, I want to show some ambiguity.
>>>>>>>>> >>>>
>>>>>>>>> >>>> >> Yes, I agree. Different definitions of certain
>>>>>>>>> distributions are confusing.
>>>>>>>>> >>>>
>>>>>>>>> >>>>>
>>>>>>>>> >>>>> The infectiousness curve you describe is a gamma
>>>>>>>>> distribution. There are two forms that it can be described by: 1) shape and
>>>>>>>>> rate, 2) shape and scale
>>>>>>>>> >>>>> https://en.wikipedia.org/wiki/Gamma_distribution
>>>>>>>>> >>>>> From your text I assume that n=8 is shape and lambda
>>>>>>>>> =1.25/day is a rate
>>>>>>>>> >>>>> So let me rewrite the function explicitly. Is the function I
>>>>>>>>> should use for infectiousness in day x:
>>>>>>>>> >>>>> f(t;a,b) = b^a*x^(a-1)*e^(-b*x) / (a-1)!
>>>>>>>>> >>>>> where a-8 and b=1.25 ?
>>>>>>>>> >>>>
>>>>>>>>> >>>> >> This is the correct representation (it's equation 7 in the
>>>>>>>>> mortality paper I shared).
>>>>>>>>> >>>>
>>>>>>>>> >>>>> If I need to implement it, do you think I can just use this
>>>>>>>>> python implementation?
>>>>>>>>> >>>>>
>>>>>>>>> https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html
>>>>>>>>> >>>>
>>>>>>>>> >>>> >> Yes, this one works, but one has to add the second
>>>>>>>>> parameter.
>>>>>>>>> >>>> Using your notation from above, I would use:
>>>>>>>>> >>>>
>>>>>>>>> >>>> from scipy.stats import gamma
>>>>>>>>> >>>> b*gamma.pdf(b*x, a)
>>>>>>>>> >>>>>
>>>>>>>>> >>>>> As for the updated zip file - I got it to work and I can see
>>>>>>>>> the plots - the incubation period plot is less interesting for me, yet the
>>>>>>>>> recovery histogram is helpful - I actually played with the number of bins
>>>>>>>>> to see the data.
>>>>>>>>> >>>>> However, I do have a few questions.
>>>>>>>>> >>>>> 1) You used Singapore data - does this data have some
>>>>>>>>> restrictions on use - meaning is there a license associated with it that
>>>>>>>>> will restrict reuse of this data for commercial purposes or redistribution
>>>>>>>>> of the data. You will have to check the terms of data usage with the origin
>>>>>>>>> - if there is a copyright symbol and no license indicating otherwise, it
>>>>>>>>> becomes a problem  we need to discuss before going public. I checked the
>>>>>>>>> web site you quoted and did not see a copyright notice, nor did I see a way
>>>>>>>>> to download the data as CSV. so I assume you can communicate with the data
>>>>>>>>> source to clarify those details.
>>>>>>>>> >>>>
>>>>>>>>> >>>> >> The data is extracted from
>>>>>>>>> https://co.vid19.sg/singapore/cases/search (Now they have more
>>>>>>>>> than 6,000 tracked cases!). It's a really underestimated source of tracked
>>>>>>>>> Covid cases.
>>>>>>>>> >>>> I've never seen any copyright symbols or licenses and tried
>>>>>>>>> to contact some health officials from Singapore last year, but without
>>>>>>>>> success. If you find some contact details, we can ask them.
>>>>>>>>> >>>>
>>>>>>>>> >>>>>
>>>>>>>>> >>>>> 2) Assuming that there is no restriction on data, you should
>>>>>>>>> still specify license on the code you created - I suggested we are doing
>>>>>>>>> this towards releasing this under CC0, yet once we add the mailing list to
>>>>>>>>> this conversation, many people can access your zip file and we need to be
>>>>>>>>> clear on what is allowed to do with each version.
>>>>>>>>> >>>>
>>>>>>>>> >>>> >> I would suggest that we first create a cleaned-up version
>>>>>>>>> of my plotting script and upload it to one of your or my GitHub repos. Then
>>>>>>>>> I'll remove the ZIP, so that others just use the clean GitHub version.
>>>>>>>>> >>>>
>>>>>>>>> >>>>>
>>>>>>>>> >>>>> If the Singapore data is already public domain and you are
>>>>>>>>> willing to release your code under CC0 - I can proceed and process your
>>>>>>>>> code and create a model I will publish for you on Github. Yet you have to
>>>>>>>>> decide if you want the zip file to become public so others can view it.
>>>>>>>>> >>>>
>>>>>>>>> >>>> >> Yes, CC0 is fine.
>>>>>>>>> >>>>
>>>>>>>>> >>>>>
>>>>>>>>> >>>>> I did not add the mailing list email since I want you to be
>>>>>>>>> ok with details before we go public. Once we clear those issues, we can
>>>>>>>>> make the conversation public. As you can see I am cautious before I make
>>>>>>>>> things public - one reason for cautiousness is to show the subgroup what is
>>>>>>>>> proper practice and how models and data should be checked for licenses.
>>>>>>>>> >>>>
>>>>>>>>> >>>> >> That's great! I think it's good to pay attention to those
>>>>>>>>> details.
>>>>>>>>> >>>>
>>>>>>>>> >>>>>
>>>>>>>>> >>>>> In any case, many thanks for this - this is progress.
>>>>>>>>> >>>>>            Jacob
>>>>>>>>> >>>>>
>>>>>>>>> >>>>> On Tue, Mar 16, 2021 at 2:11 AM LUCAS BOETTCHER <
>>>>>>>>> lucasb at g.ucla.edu> wrote:
>>>>>>>>> >>>>>>
>>>>>>>>> >>>>>> Hi Jacob
>>>>>>>>> >>>>>> Yes, I meant equation 16 not 18 in [1]. This equation
>>>>>>>>> describes the infectiousness \beta(\tau) as a function of the time since
>>>>>>>>> infection \tau. The distribution parameters are as specified in my previous
>>>>>>>>> email and also described in [1].
>>>>>>>>> >>>>>> I updated the ZIP:
>>>>>>>>> http://lucas-boettcher.info/downloads/singapore_.zip
>>>>>>>>> >>>>>> There is no need anymore to have Latex connected to python
>>>>>>>>> to run this script. I'll add a YML environment file next time.
>>>>>>>>> >>>>>> I am fine with releasing everything I shared under CC0;
>>>>>>>>> please feel free to add our discussion to the mailing list.
>>>>>>>>> >>>>>> Best
>>>>>>>>> >>>>>> Lucas
>>>>>>>>> >>>>>>
>>>>>>>>> >>>>>> ---
>>>>>>>>> >>>>>> [1] Böttcher, L., Xia, M., & Chou, T. (2020). Why case
>>>>>>>>> fatality ratios can be misleading: individual-and population-based
>>>>>>>>> mortality estimates and factors influencing them. Physical Biology, 17(6),
>>>>>>>>> 065003.
>>>>>>>>> >>>>>> On Sun, Mar 14, 2021 at 6:35 PM LUCAS BOETTCHER <
>>>>>>>>> lucasb at g.ucla.edu> wrote:
>>>>>>>>> >>>>>>>
>>>>>>>>> >>>>>>> Hi Jacob
>>>>>>>>> >>>>>>> In [1] (Eq. 18) we used the gamma distribution
>>>>>>>>> >>>>>>> \beta(\tau)=\beta_0 \rho(\tau;n,\lambda),
>>>>>>>>> >>>>>>> to describe an infectiousness profile estimate from [2].
>>>>>>>>> Here, \tau is the time since infection, n=8 (shape parameter), and
>>>>>>>>> \lambda=1.25/day (rate parameter). The amplitude \beta_0 S_0 can be
>>>>>>>>> estimated using R_0 estimates (see [1]).
>>>>>>>>> >>>>>>> Incubation period and recovery time profiles (incl. data
>>>>>>>>> from https://co.vid19.sg/cases) are stored here:
>>>>>>>>> http://lucas-boettcher.info/downloads/singapore_.zip
>>>>>>>>> >>>>>>> (I'll remove the ZIP in a few weeks, but you can download
>>>>>>>>> and store the data somewhere else if it's helpful for your research.)
>>>>>>>>> >>>>>>>
>>>>>>>>> >>>>>>> And regarding the license issue, please let me know what
>>>>>>>>> would be best for your work. I am not sure if CC0 might be the best
>>>>>>>>> solution for you:
>>>>>>>>> >>>>>>>
>>>>>>>>> https://opensource.stackexchange.com/questions/133/how-could-using-code-released-under-cc0-infringe-on-the-authors-patents
>>>>>>>>> >>>>>>> Best
>>>>>>>>> >>>>>>> Lucas
>>>>>>>>> >>>>>>> ---
>>>>>>>>> >>>>>>> [1] Böttcher, L., Xia, M., & Chou, T. (2020). Why case
>>>>>>>>> fatality ratios can be misleading: individual-and population-based
>>>>>>>>> mortality estimates and factors influencing them. Physical Biology, 17(6),
>>>>>>>>> 065003.
>>>>>>>>> >>>>>>> [2] He, X., Lau, E. H., Wu, P., Deng, X., Wang, J., Hao,
>>>>>>>>> X., ... & Leung, G. M. (2020). Temporal dynamics in viral shedding and
>>>>>>>>> transmissibility of COVID-19. Nature medicine, 26(5), 672-675.
>>>>>>>>
>>>>>>>> _______________________________________________
> Vp-integration-subgroup mailing list
> Vp-integration-subgroup at lists.simtk.org
> https://lists.simtk.org/mailman/listinfo/vp-integration-subgroup
>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://lists.simtk.org/pipermail/vp-integration-subgroup/attachments/20210429/89fbc9eb/attachment-0001.html>

More information about the Vp-integration-subgroup mailing list